
In various computer vision problems, obtaining labeled data specifically tailored for a new task (be it a new domain or a new class) can be challenging due to one or more of several reasons: high annotation costs, dynamic addition of objects with new semantic content, limited instances of rare objects or long-tailed distributions which frequently occur in real-world scenarios [1]}. To address such issues, two popular recent approaches include: (i) zero-shot learning (ZSL): use training data of related object categories from the same domain (e.g, sketches of cats as a training data for recognizing dogs from sketches); and (ii) domain generalization (DG): use training data of a particular object category from related domains (e.g, photos/real images of dogs as a training data for recognizing dogs from sketches). More recently, there has been increasing interest in a combination of these approaches to handle unseen classes in unseen domains, viz. zero-shot domain generalization (which we call ZSLDG), where one leverages training data of a related object category from a related domain (e.g, photos of cats as a training data for recognizing dogs from sketches). DG addresses only domain shift that occurs due to the training and test domains being different. ZSL addresses semantic shift that occurs due to the presence of different object categories during training and testing. In contrast, ZSLDG more closely aligns with the challenges faced in real-world applications, but needs to simultaneously address domain and semantic shift issues [2]}, [3]}.
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